Overview

Dataset statistics

Number of variables16
Number of observations13184
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory143.0 B

Variable types

Numeric15
Categorical1

Alerts

song_name has a high cardinality: 9893 distinct values High cardinality
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
energy is highly correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly correlated with acousticness and 2 other fieldsHigh correlation
instrumentalness is highly correlated with loudnessHigh correlation
tempo is highly correlated with time_signatureHigh correlation
time_signature is highly correlated with tempoHigh correlation
song_name is uniformly distributed Uniform
song_id has unique values Unique
song_popularity has 173 (1.3%) zeros Zeros
instrumentalness has 5043 (38.3%) zeros Zeros
key has 1520 (11.5%) zeros Zeros
audio_mode has 4902 (37.2%) zeros Zeros

Reproduction

Analysis started2022-11-24 19:08:33.259914
Analysis finished2022-11-24 19:09:06.220070
Duration32.96 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

song_id
Real number (ℝ≥0)

UNIQUE

Distinct13184
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9397.536104
Minimum0
Maximum18832
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T20:09:06.328068image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile938.15
Q14690.75
median9406
Q314090.5
95-th percentile17940.85
Maximum18832
Range18832
Interquartile range (IQR)9399.75

Descriptive statistics

Standard deviation5443.026435
Coefficient of variation (CV)0.5791971826
Kurtosis-1.195766623
Mean9397.536104
Median Absolute Deviation (MAD)4701.5
Skewness0.009096673618
Sum123897116
Variance29626536.77
MonotonicityNot monotonic
2022-11-24T20:09:06.459068image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22311
 
< 0.1%
165831
 
< 0.1%
169081
 
< 0.1%
101551
 
< 0.1%
181501
 
< 0.1%
166941
 
< 0.1%
3311
 
< 0.1%
187021
 
< 0.1%
163641
 
< 0.1%
179951
 
< 0.1%
Other values (13174)13174
99.9%
ValueCountFrequency (%)
01
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
71
< 0.1%
81
< 0.1%
101
< 0.1%
111
< 0.1%
121
< 0.1%
141
< 0.1%
ValueCountFrequency (%)
188321
< 0.1%
188301
< 0.1%
188291
< 0.1%
188281
< 0.1%
188271
< 0.1%
188251
< 0.1%
188241
< 0.1%
188221
< 0.1%
188211
< 0.1%
188201
< 0.1%

song_name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct9893
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size103.1 KiB
Better
 
16
Promises (with Sam Smith)
 
13
FEFE (feat. Nicki Minaj & Murda Beatz)
 
12
Without Me
 
12
REEL IT IN
 
11
Other values (9888)
13120 

Length

Max length141
Median length83
Mean length16.63425364
Min length1

Characters and Unicode

Total characters219306
Distinct characters255
Distinct categories16 ?
Distinct scripts8 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8030 ?
Unique (%)60.9%

Sample

1st rowManeater - Radio Edit
2nd rowBetter Off Alone
3rd rowSong That I Heard
4th rowZumba
5th rowBumper To Bumper

Common Values

ValueCountFrequency (%)
Better16
 
0.1%
Promises (with Sam Smith)13
 
0.1%
FEFE (feat. Nicki Minaj & Murda Beatz)12
 
0.1%
Without Me12
 
0.1%
REEL IT IN11
 
0.1%
I Like It11
 
0.1%
Taki Taki (with Selena Gomez, Ozuna & Cardi B)11
 
0.1%
MIA (feat. Drake)11
 
0.1%
ZEZE (feat. Travis Scott & Offset)11
 
0.1%
Sunflower - Spider-Man: Into the Spider-Verse10
 
0.1%
Other values (9883)13066
99.1%

Length

2022-11-24T20:09:06.617578image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1716
 
4.1%
the1132
 
2.7%
feat1051
 
2.5%
you741
 
1.8%
me634
 
1.5%
i517
 
1.2%
love477
 
1.1%
my377
 
0.9%
a373
 
0.9%
in356
 
0.8%
Other values (8104)34759
82.5%

Most occurring characters

ValueCountFrequency (%)
28949
 
13.2%
e20370
 
9.3%
a13261
 
6.0%
o13144
 
6.0%
i11174
 
5.1%
t10150
 
4.6%
n10052
 
4.6%
r8990
 
4.1%
l6754
 
3.1%
s6708
 
3.1%
Other values (245)89754
40.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter139886
63.8%
Uppercase Letter39954
 
18.2%
Space Separator28949
 
13.2%
Other Punctuation3703
 
1.7%
Close Punctuation1709
 
0.8%
Open Punctuation1709
 
0.8%
Decimal Number1679
 
0.8%
Dash Punctuation1458
 
0.7%
Other Letter152
 
0.1%
Currency Symbol41
 
< 0.1%
Other values (6)66
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا9
 
5.9%
ج3
 
2.0%
ر3
 
2.0%
د3
 
2.0%
ي3
 
2.0%
ل3
 
2.0%
2
 
1.3%
2
 
1.3%
2
 
1.3%
2
 
1.3%
Other values (113)120
78.9%
Lowercase Letter
ValueCountFrequency (%)
e20370
14.6%
a13261
 
9.5%
o13144
 
9.4%
i11174
 
8.0%
t10150
 
7.3%
n10052
 
7.2%
r8990
 
6.4%
l6754
 
4.8%
s6708
 
4.8%
u5005
 
3.6%
Other values (41)34278
24.5%
Uppercase Letter
ValueCountFrequency (%)
S3206
 
8.0%
T3040
 
7.6%
M2994
 
7.5%
L2574
 
6.4%
B2488
 
6.2%
A2186
 
5.5%
R2062
 
5.2%
I2038
 
5.1%
D1959
 
4.9%
C1900
 
4.8%
Other values (22)15507
38.8%
Other Punctuation
ValueCountFrequency (%)
.1519
41.0%
'1065
28.8%
&360
 
9.7%
,342
 
9.2%
!90
 
2.4%
/89
 
2.4%
?79
 
2.1%
"64
 
1.7%
*34
 
0.9%
:34
 
0.9%
Other values (7)27
 
0.7%
Decimal Number
ValueCountFrequency (%)
0365
21.7%
1342
20.4%
2333
19.8%
9181
10.8%
595
 
5.7%
484
 
5.0%
378
 
4.6%
668
 
4.1%
867
 
4.0%
766
 
3.9%
Close Punctuation
ValueCountFrequency (%)
)1672
97.8%
]35
 
2.0%
2
 
0.1%
Open Punctuation
ValueCountFrequency (%)
(1672
97.8%
[35
 
2.0%
2
 
0.1%
Currency Symbol
ValueCountFrequency (%)
$39
95.1%
¥1
 
2.4%
£1
 
2.4%
Math Symbol
ValueCountFrequency (%)
+7
53.8%
|5
38.5%
<1
 
7.7%
Final Punctuation
ValueCountFrequency (%)
35
89.7%
4
 
10.3%
Initial Punctuation
ValueCountFrequency (%)
4
80.0%
1
 
20.0%
Other Symbol
ValueCountFrequency (%)
®3
75.0%
°1
 
25.0%
Space Separator
ValueCountFrequency (%)
28949
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1458
100.0%
Modifier Symbol
ValueCountFrequency (%)
´3
100.0%
Modifier Letter
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin179834
82.0%
Common39314
 
17.9%
Han75
 
< 0.1%
Arabic37
 
< 0.1%
Katakana20
 
< 0.1%
Hangul20
 
< 0.1%
Cyrillic5
 
< 0.1%
Greek1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e20370
 
11.3%
a13261
 
7.4%
o13144
 
7.3%
i11174
 
6.2%
t10150
 
5.6%
n10052
 
5.6%
r8990
 
5.0%
l6754
 
3.8%
s6708
 
3.7%
u5005
 
2.8%
Other values (67)74226
41.3%
Han
ValueCountFrequency (%)
2
 
2.7%
2
 
2.7%
2
 
2.7%
2
 
2.7%
2
 
2.7%
2
 
2.7%
1
 
1.3%
1
 
1.3%
1
 
1.3%
1
 
1.3%
Other values (59)59
78.7%
Common
ValueCountFrequency (%)
28949
73.6%
)1672
 
4.3%
(1672
 
4.3%
.1519
 
3.9%
-1458
 
3.7%
'1065
 
2.7%
0365
 
0.9%
&360
 
0.9%
,342
 
0.9%
1342
 
0.9%
Other values (39)1570
 
4.0%
Hangul
ValueCountFrequency (%)
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (10)10
50.0%
Katakana
ValueCountFrequency (%)
2
 
10.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (9)9
45.0%
Arabic
ValueCountFrequency (%)
ا9
24.3%
ج3
 
8.1%
ر3
 
8.1%
د3
 
8.1%
ي3
 
8.1%
ل3
 
8.1%
و2
 
5.4%
ك2
 
5.4%
ع2
 
5.4%
ز2
 
5.4%
Other values (5)5
13.5%
Cyrillic
ValueCountFrequency (%)
к1
20.0%
и1
20.0%
л1
20.0%
е1
20.0%
г1
20.0%
Greek
ValueCountFrequency (%)
ύ1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII218745
99.7%
None353
 
0.2%
CJK75
 
< 0.1%
Punctuation47
 
< 0.1%
Arabic37
 
< 0.1%
Katakana24
 
< 0.1%
Hangul20
 
< 0.1%
Cyrillic5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28949
 
13.2%
e20370
 
9.3%
a13261
 
6.1%
o13144
 
6.0%
i11174
 
5.1%
t10150
 
4.6%
n10052
 
4.6%
r8990
 
4.1%
l6754
 
3.1%
s6708
 
3.1%
Other values (75)89193
40.8%
None
ValueCountFrequency (%)
é81
22.9%
í52
14.7%
ó51
14.4%
á40
11.3%
ñ34
9.6%
ú26
 
7.4%
¿9
 
2.5%
ë6
 
1.7%
Ü5
 
1.4%
ê5
 
1.4%
Other values (25)44
12.5%
Punctuation
ValueCountFrequency (%)
35
74.5%
4
 
8.5%
4
 
8.5%
3
 
6.4%
1
 
2.1%
Arabic
ValueCountFrequency (%)
ا9
24.3%
ج3
 
8.1%
ر3
 
8.1%
د3
 
8.1%
ي3
 
8.1%
ل3
 
8.1%
و2
 
5.4%
ك2
 
5.4%
ع2
 
5.4%
ز2
 
5.4%
Other values (5)5
13.5%
CJK
ValueCountFrequency (%)
2
 
2.7%
2
 
2.7%
2
 
2.7%
2
 
2.7%
2
 
2.7%
2
 
2.7%
1
 
1.3%
1
 
1.3%
1
 
1.3%
1
 
1.3%
Other values (59)59
78.7%
Katakana
ValueCountFrequency (%)
2
 
8.3%
2
 
8.3%
2
 
8.3%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
Other values (11)11
45.8%
Hangul
ValueCountFrequency (%)
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (10)10
50.0%
Cyrillic
ValueCountFrequency (%)
к1
20.0%
и1
20.0%
л1
20.0%
е1
20.0%
г1
20.0%

song_popularity
Real number (ℝ≥0)

ZEROS

Distinct101
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.07190534
Minimum0
Maximum100
Zeros173
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T20:09:06.766543image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q140
median56
Q369
95-th percentile85
Maximum100
Range100
Interquartile range (IQR)29

Descriptive statistics

Standard deviation21.76967318
Coefficient of variation (CV)0.4101920412
Kurtosis-0.1602217316
Mean53.07190534
Median Absolute Deviation (MAD)14
Skewness-0.504843156
Sum699700
Variance473.9186705
MonotonicityNot monotonic
2022-11-24T20:09:06.900971image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58292
 
2.2%
60279
 
2.1%
63270
 
2.0%
66270
 
2.0%
70269
 
2.0%
55269
 
2.0%
62267
 
2.0%
52266
 
2.0%
61261
 
2.0%
53260
 
2.0%
Other values (91)10481
79.5%
ValueCountFrequency (%)
0173
1.3%
175
0.6%
276
0.6%
347
 
0.4%
467
 
0.5%
553
 
0.4%
650
 
0.4%
759
 
0.4%
861
 
0.5%
940
 
0.3%
ValueCountFrequency (%)
1007
 
0.1%
9910
 
0.1%
9832
0.2%
9727
0.2%
9636
0.3%
9542
0.3%
9454
0.4%
9322
 
0.2%
9243
0.3%
9166
0.5%

song_duration_ms
Real number (ℝ≥0)

Distinct9117
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218513.6007
Minimum12000
Maximum1799346
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T20:09:07.035975image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum12000
5-th percentile141970.7
Q1184876.5
median211794
Q3243160
95-th percentile313545.25
Maximum1799346
Range1787346
Interquartile range (IQR)58283.5

Descriptive statistics

Standard deviation60074.78459
Coefficient of variation (CV)0.2749246931
Kurtosis52.99166553
Mean218513.6007
Median Absolute Deviation (MAD)28806
Skewness3.48211507
Sum2880883311
Variance3608979744
MonotonicityNot monotonic
2022-11-24T20:09:07.166972image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16500019
 
0.1%
18000016
 
0.1%
19500014
 
0.1%
15200014
 
0.1%
21330913
 
0.1%
17940412
 
0.1%
21250012
 
0.1%
18900012
 
0.1%
21036711
 
0.1%
12134611
 
0.1%
Other values (9107)13050
99.0%
ValueCountFrequency (%)
120001
< 0.1%
313731
< 0.1%
359201
< 0.1%
500141
< 0.1%
505081
< 0.1%
505731
< 0.1%
530661
< 0.1%
545391
< 0.1%
552131
< 0.1%
557201
< 0.1%
ValueCountFrequency (%)
17993461
< 0.1%
12336661
< 0.1%
10479331
< 0.1%
8334931
< 0.1%
8295861
< 0.1%
8057461
< 0.1%
7609731
< 0.1%
7472221
< 0.1%
7456531
< 0.1%
7361601
< 0.1%

acousticness
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2918
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2586286004
Minimum1.02 × 10-6
Maximum0.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T20:09:07.315973image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1.02 × 10-6
5-th percentile0.00070945
Q10.0251
median0.132
Q30.422
95-th percentile0.883
Maximum0.996
Range0.99599898
Interquartile range (IQR)0.3969

Descriptive statistics

Standard deviation0.2886015434
Coefficient of variation (CV)1.115891834
Kurtosis-0.08806190881
Mean0.2586286004
Median Absolute Deviation (MAD)0.12577
Skewness1.074590145
Sum3409.759468
Variance0.08329085085
MonotonicityNot monotonic
2022-11-24T20:09:07.451685image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1332
 
0.2%
0.10529
 
0.2%
0.15329
 
0.2%
0.021528
 
0.2%
0.10728
 
0.2%
0.10228
 
0.2%
0.17328
 
0.2%
0.13526
 
0.2%
0.14126
 
0.2%
0.11926
 
0.2%
Other values (2908)12904
97.9%
ValueCountFrequency (%)
1.02e-061
< 0.1%
1.37e-061
< 0.1%
1.4e-061
< 0.1%
1.8e-061
< 0.1%
1.95e-061
< 0.1%
2.18e-061
< 0.1%
2.42e-061
< 0.1%
3.19e-061
< 0.1%
3.26e-061
< 0.1%
3.41e-061
< 0.1%
ValueCountFrequency (%)
0.99610
0.1%
0.99518
0.1%
0.99412
0.1%
0.99319
0.1%
0.9929
0.1%
0.99110
0.1%
0.9911
0.1%
0.9896
 
< 0.1%
0.98810
0.1%
0.9875
 
< 0.1%

danceability
Real number (ℝ≥0)

Distinct824
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6337836772
Minimum0
Maximum0.987
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T20:09:07.588688image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.357
Q10.534
median0.646
Q30.748
95-th percentile0.875
Maximum0.987
Range0.987
Interquartile range (IQR)0.214

Descriptive statistics

Standard deviation0.1558383256
Coefficient of variation (CV)0.2458856723
Kurtosis-0.0473754053
Mean0.6337836772
Median Absolute Deviation (MAD)0.106
Skewness-0.3953997516
Sum8355.804
Variance0.02428558372
MonotonicityNot monotonic
2022-11-24T20:09:07.724683image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.68750
 
0.4%
0.65750
 
0.4%
0.649
 
0.4%
0.65949
 
0.4%
0.694000000000000148
 
0.4%
0.64347
 
0.4%
0.75747
 
0.4%
0.61146
 
0.3%
0.75545
 
0.3%
0.70844
 
0.3%
Other values (814)12709
96.4%
ValueCountFrequency (%)
0.02
< 0.1%
0.06171
< 0.1%
0.06251
< 0.1%
0.0661
< 0.1%
0.06841
< 0.1%
0.07221
< 0.1%
0.0811
< 0.1%
0.08121
< 0.1%
0.08331
< 0.1%
0.08551
< 0.1%
ValueCountFrequency (%)
0.9871
 
< 0.1%
0.9811
 
< 0.1%
0.9781
 
< 0.1%
0.9752
 
< 0.1%
0.9721
 
< 0.1%
0.9691
 
< 0.1%
0.9681
 
< 0.1%
0.9676
< 0.1%
0.9661
 
< 0.1%
0.9655
< 0.1%

energy
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1058
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6456502761
Minimum0.00107
Maximum0.997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T20:09:07.873685image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.00107
5-th percentile0.234
Q10.512
median0.6745
Q30.816
95-th percentile0.94
Maximum0.997
Range0.99593
Interquartile range (IQR)0.304

Descriptive statistics

Standard deviation0.2140981311
Coefficient of variation (CV)0.3316007737
Kurtosis-0.121819588
Mean0.6456502761
Median Absolute Deviation (MAD)0.1505
Skewness-0.6291780527
Sum8512.25324
Variance0.04583800974
MonotonicityNot monotonic
2022-11-24T20:09:08.007686image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.717000000000000143
 
0.3%
0.704000000000000143
 
0.3%
0.785999999999999939
 
0.3%
0.7339
 
0.3%
0.90239
 
0.3%
0.6337
 
0.3%
0.78537
 
0.3%
0.67537
 
0.3%
0.71636
 
0.3%
0.7536
 
0.3%
Other values (1048)12798
97.1%
ValueCountFrequency (%)
0.001071
< 0.1%
0.001631
< 0.1%
0.002051
< 0.1%
0.002661
< 0.1%
0.003051
< 0.1%
0.003441
< 0.1%
0.003621
< 0.1%
0.003792
< 0.1%
0.004191
< 0.1%
0.004651
< 0.1%
ValueCountFrequency (%)
0.9973
 
< 0.1%
0.9965
 
< 0.1%
0.9952
 
< 0.1%
0.9943
 
< 0.1%
0.9935
 
< 0.1%
0.9923
 
< 0.1%
0.99112
0.1%
0.9910
0.1%
0.98913
0.1%
0.98812
0.1%

instrumentalness
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct3410
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07706328421
Minimum0
Maximum0.997
Zeros5043
Zeros (%)38.3%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T20:09:08.143108image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.085 × 10-5
Q30.0025325
95-th percentile0.78085
Maximum0.997
Range0.997
Interquartile range (IQR)0.0025325

Descriptive statistics

Standard deviation0.2204576457
Coefficient of variation (CV)2.860735147
Kurtosis7.772678879
Mean0.07706328421
Median Absolute Deviation (MAD)1.085 × 10-5
Skewness3.018030901
Sum1016.002339
Variance0.04860157353
MonotonicityNot monotonic
2022-11-24T20:09:08.277742image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05043
38.3%
3.33e-0623
 
0.2%
1.16e-0614
 
0.1%
0.0010714
 
0.1%
4.91e-0613
 
0.1%
0.0012513
 
0.1%
1.03e-0513
 
0.1%
0.0011412
 
0.1%
1.07e-0512
 
0.1%
0.39111
 
0.1%
Other values (3400)8016
60.8%
ValueCountFrequency (%)
0.05043
38.3%
1e-062
 
< 0.1%
1.01e-063
 
< 0.1%
1.02e-066
 
< 0.1%
1.03e-065
 
< 0.1%
1.04e-068
 
0.1%
1.05e-063
 
< 0.1%
1.06e-063
 
< 0.1%
1.07e-066
 
< 0.1%
1.08e-064
 
< 0.1%
ValueCountFrequency (%)
0.9971
< 0.1%
0.9891
< 0.1%
0.9821
< 0.1%
0.9791
< 0.1%
0.9751
< 0.1%
0.9741
< 0.1%
0.9732
< 0.1%
0.9721
< 0.1%
0.9711
< 0.1%
0.9692
< 0.1%

key
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.260087985
Minimum0
Maximum11
Zeros1520
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T20:09:08.391968image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.616056868
Coefficient of variation (CV)0.6874517838
Kurtosis-1.312696076
Mean5.260087985
Median Absolute Deviation (MAD)3
Skewness0.01049325572
Sum69349
Variance13.07586728
MonotonicityNot monotonic
2022-11-24T20:09:08.483979image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11576
12.0%
01520
11.5%
71430
10.8%
21207
9.2%
91178
8.9%
111129
8.6%
51085
8.2%
6966
7.3%
8931
7.1%
4922
7.0%
Other values (2)1240
9.4%
ValueCountFrequency (%)
01520
11.5%
11576
12.0%
21207
9.2%
3347
 
2.6%
4922
7.0%
51085
8.2%
6966
7.3%
71430
10.8%
8931
7.1%
91178
8.9%
ValueCountFrequency (%)
111129
8.6%
10893
6.8%
91178
8.9%
8931
7.1%
71430
10.8%
6966
7.3%
51085
8.2%
4922
7.0%
3347
 
2.6%
21207
9.2%

liveness
Real number (ℝ≥0)

Distinct1351
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1788014336
Minimum0.0109
Maximum0.986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T20:09:08.604072image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.0109
5-th percentile0.0572
Q10.0927
median0.121
Q30.22
95-th percentile0.463
Maximum0.986
Range0.9751
Interquartile range (IQR)0.1273

Descriptive statistics

Standard deviation0.1436617527
Coefficient of variation (CV)0.8034709222
Kurtosis5.823515265
Mean0.1788014336
Median Absolute Deviation (MAD)0.04105
Skewness2.22442847
Sum2357.3181
Variance0.02063869919
MonotonicityNot monotonic
2022-11-24T20:09:08.736080image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.108146
 
1.1%
0.106141
 
1.1%
0.104140
 
1.1%
0.102135
 
1.0%
0.111135
 
1.0%
0.11132
 
1.0%
0.112132
 
1.0%
0.109131
 
1.0%
0.107130
 
1.0%
0.105125
 
0.9%
Other values (1341)11837
89.8%
ValueCountFrequency (%)
0.01091
 
< 0.1%
0.01481
 
< 0.1%
0.01861
 
< 0.1%
0.01931
 
< 0.1%
0.01963
< 0.1%
0.02061
 
< 0.1%
0.02191
 
< 0.1%
0.02227
0.1%
0.02332
 
< 0.1%
0.02371
 
< 0.1%
ValueCountFrequency (%)
0.9861
 
< 0.1%
0.9811
 
< 0.1%
0.9791
 
< 0.1%
0.9782
< 0.1%
0.9741
 
< 0.1%
0.9671
 
< 0.1%
0.9611
 
< 0.1%
0.9561
 
< 0.1%
0.9523
< 0.1%
0.952
< 0.1%

loudness
Real number (ℝ)

HIGH CORRELATION

Distinct6976
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.445227321
Minimum-38.768
Maximum1.585
Zeros0
Zeros (%)0.0%
Negative13178
Negative (%)> 99.9%
Memory size116.0 KiB
2022-11-24T20:09:08.870481image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-38.768
5-th percentile-14.3307
Q1-9.03125
median-6.5505
Q3-4.897
95-th percentile-3.154
Maximum1.585
Range40.353
Interquartile range (IQR)4.13425

Descriptive statistics

Standard deviation3.844634631
Coefficient of variation (CV)-0.5163891531
Kurtosis6.709971793
Mean-7.445227321
Median Absolute Deviation (MAD)1.9185
Skewness-1.956156555
Sum-98157.877
Variance14.78121544
MonotonicityNot monotonic
2022-11-24T20:09:09.015478image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.991000000000000514
 
0.1%
-4.88413
 
0.1%
-6.04413
 
0.1%
-6.24612
 
0.1%
-3.71412
 
0.1%
-6.43912
 
0.1%
-5.33512
 
0.1%
-9.12712
 
0.1%
-4.58912
 
0.1%
-4.20612
 
0.1%
Other values (6966)13060
99.1%
ValueCountFrequency (%)
-38.7681
< 0.1%
-36.7291
< 0.1%
-36.2811
< 0.1%
-35.4491
< 0.1%
-35.3891
< 0.1%
-34.2551
< 0.1%
-33.8591
< 0.1%
-33.2461
< 0.1%
-32.1951
< 0.1%
-31.9211
< 0.1%
ValueCountFrequency (%)
1.5851
< 0.1%
1.3421
< 0.1%
0.8781
< 0.1%
0.5251
< 0.1%
0.1981
< 0.1%
0.1191
< 0.1%
-0.2571
< 0.1%
-0.3981
< 0.1%
-0.7371
< 0.1%
-0.73900000000000011
< 0.1%

audio_mode
Real number (ℝ≥0)

ZEROS

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6281856796
Minimum0
Maximum1
Zeros4902
Zeros (%)37.2%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T20:09:09.132091image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4833075097
Coefficient of variation (CV)0.769370467
Kurtosis-1.718796216
Mean0.6281856796
Median Absolute Deviation (MAD)0
Skewness-0.5305323242
Sum8282
Variance0.2335861489
MonotonicityNot monotonic
2022-11-24T20:09:09.220094image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
18282
62.8%
04902
37.2%
ValueCountFrequency (%)
04902
37.2%
18282
62.8%
ValueCountFrequency (%)
18282
62.8%
04902
37.2%

speechiness
Real number (ℝ≥0)

Distinct1192
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1012050743
Minimum0
Maximum0.94
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T20:09:09.338125image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0289
Q10.0378
median0.0553
Q30.117
95-th percentile0.33285
Maximum0.94
Range0.94
Interquartile range (IQR)0.0792

Descriptive statistics

Standard deviation0.1040103901
Coefficient of variation (CV)1.027719121
Kurtosis7.020972154
Mean0.1012050743
Median Absolute Deviation (MAD)0.0224
Skewness2.341128745
Sum1334.2877
Variance0.01081816124
MonotonicityNot monotonic
2022-11-24T20:09:09.470091image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.033746
 
0.3%
0.03246
 
0.3%
0.03144
 
0.3%
0.10944
 
0.3%
0.031842
 
0.3%
0.036241
 
0.3%
0.043940
 
0.3%
0.039440
 
0.3%
0.03839
 
0.3%
0.035739
 
0.3%
Other values (1182)12763
96.8%
ValueCountFrequency (%)
0.02
< 0.1%
0.02282
< 0.1%
0.02291
 
< 0.1%
0.02313
< 0.1%
0.02342
< 0.1%
0.02351
 
< 0.1%
0.02363
< 0.1%
0.02382
< 0.1%
0.02393
< 0.1%
0.0241
 
< 0.1%
ValueCountFrequency (%)
0.941
< 0.1%
0.9361
< 0.1%
0.9151
< 0.1%
0.9061
< 0.1%
0.8941
< 0.1%
0.89099999999999991
< 0.1%
0.86900000000000011
< 0.1%
0.8312
< 0.1%
0.831
< 0.1%
0.8261
< 0.1%

tempo
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9331
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.0079172
Minimum0
Maximum214.686
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T20:09:09.611094image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79.10505
Q198.8705
median120.013
Q3139.934
95-th percentile174.07265
Maximum214.686
Range214.686
Interquartile range (IQR)41.0635

Descriptive statistics

Standard deviation28.66379584
Coefficient of variation (CV)0.2368753755
Kurtosis-0.21119189
Mean121.0079172
Median Absolute Deviation (MAD)20.035
Skewness0.4299170691
Sum1595368.381
Variance821.6131919
MonotonicityNot monotonic
2022-11-24T20:09:09.734552image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120.01314
 
0.1%
123.0714
 
0.1%
128.0379999999999813
 
0.1%
150.03612
 
0.1%
125.97812
 
0.1%
149.98912
 
0.1%
97.06411
 
0.1%
136.04811
 
0.1%
95.94811
 
0.1%
122.00711
 
0.1%
Other values (9321)13063
99.1%
ValueCountFrequency (%)
0.02
< 0.1%
46.5911
< 0.1%
51.6071
< 0.1%
56.9831
< 0.1%
56.9851
< 0.1%
57.01
< 0.1%
57.3041
< 0.1%
57.5231
< 0.1%
58.0171
< 0.1%
59.541
< 0.1%
ValueCountFrequency (%)
214.6861
< 0.1%
213.991
< 0.1%
213.2261
< 0.1%
212.0581
< 0.1%
211.6441
< 0.1%
211.3571
< 0.1%
210.751
< 0.1%
209.4211
< 0.1%
208.9691
< 0.1%
208.7061
< 0.1%

time_signature
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.960406553
Minimum0
Maximum5
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T20:09:09.835552image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14
median4
Q34
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2985885811
Coefficient of variation (CV)0.07539341658
Kurtosis48.84734404
Mean3.960406553
Median Absolute Deviation (MAD)0
Skewness-5.225535555
Sum52214
Variance0.08915514078
MonotonicityNot monotonic
2022-11-24T20:09:09.925565image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
412457
94.5%
3511
 
3.9%
5160
 
1.2%
153
 
0.4%
03
 
< 0.1%
ValueCountFrequency (%)
03
 
< 0.1%
153
 
0.4%
3511
 
3.9%
412457
94.5%
5160
 
1.2%
ValueCountFrequency (%)
5160
 
1.2%
412457
94.5%
3511
 
3.9%
153
 
0.4%
03
 
< 0.1%

audio_valence
Real number (ℝ≥0)

Distinct1181
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5284147072
Minimum0
Maximum0.984
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T20:09:10.043000image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12815
Q10.332
median0.528
Q30.728
95-th percentile0.92485
Maximum0.984
Range0.984
Interquartile range (IQR)0.396

Descriptive statistics

Standard deviation0.2464026757
Coefficient of variation (CV)0.4663054838
Kurtosis-0.9908645603
Mean0.5284147072
Median Absolute Deviation (MAD)0.198
Skewness-0.02231442083
Sum6966.6195
Variance0.0607142786
MonotonicityNot monotonic
2022-11-24T20:09:10.181003image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.96152
 
0.4%
0.96445
 
0.3%
0.50539
 
0.3%
0.59139
 
0.3%
0.39936
 
0.3%
0.96234
 
0.3%
0.37634
 
0.3%
0.532999999999999933
 
0.3%
0.9632
 
0.2%
0.32931
 
0.2%
Other values (1171)12809
97.2%
ValueCountFrequency (%)
0.02
< 0.1%
0.0231
< 0.1%
0.02771
< 0.1%
0.02922
< 0.1%
0.03121
< 0.1%
0.03161
< 0.1%
0.03211
< 0.1%
0.03262
< 0.1%
0.03291
< 0.1%
0.0331
< 0.1%
ValueCountFrequency (%)
0.9841
 
< 0.1%
0.9823
< 0.1%
0.9812
< 0.1%
0.981
 
< 0.1%
0.9792
< 0.1%
0.9781
 
< 0.1%
0.9773
< 0.1%
0.9764
< 0.1%
0.9753
< 0.1%
0.9742
< 0.1%

Interactions

2022-11-24T20:09:03.902623image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:37.783287image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:39.799703image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:41.543063image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:43.621432image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:45.419970image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:47.188918image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:49.121196image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:50.916784image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:52.633106image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:54.400106image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:56.556914image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:58.325863image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:00.122312image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:01.894855image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:04.023658image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:37.953272image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:39.920398image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:41.675496image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:43.742949image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:45.542636image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:47.308920image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:49.246195image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:51.037667image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:52.754137image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:54.528085image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:56.678665image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:58.454862image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:00.242277image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:02.020055image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:04.140652image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:38.078904image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:40.034575image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:41.799726image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:43.860037image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:45.660764image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:47.421653image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:49.372835image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:51.150971image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:52.869101image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:54.896947image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:56.791584image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:58.567765image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:00.355891image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:02.132764image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:04.266656image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:38.210718image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:40.158936image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:41.935190image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:43.989475image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:45.785897image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:47.545482image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:49.509847image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:51.275973image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:52.993138image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:55.026830image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:56.917620image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:58.690764image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:00.479548image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:02.257762image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:04.382623image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:38.456731image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:40.273817image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:42.058943image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:44.107148image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:45.902761image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:47.661522image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:49.630851image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:51.389971image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:53.109140image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:55.151625image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:57.032619image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:58.809766image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:00.593262image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:02.371793image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:04.499654image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:38.578815image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:40.389561image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:42.183340image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:44.226013image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:46.023569image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:47.778557image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:49.747875image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:51.506940image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:53.224667image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:55.274099image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:57.148492image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:58.929902image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:00.708217image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:02.496758image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:04.622620image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:38.698486image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:40.503343image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:42.306148image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:44.354528image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:46.138454image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:47.892464image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:49.863845image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:51.623974image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:53.336656image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:55.429096image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:57.263495image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:59.043898image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:00.824220image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:02.611773image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:04.744621image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:38.823858image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:40.621379image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:42.432027image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:44.477257image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:46.254104image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:48.006026image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:49.980876image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:51.736675image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:53.454145image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:55.550129image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:57.378467image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:59.159899image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:00.971251image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:02.728792image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:04.854620image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:38.943765image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:40.734210image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:42.552756image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:44.589845image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:46.367807image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:48.297995image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:50.094019image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:51.846674image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:53.567147image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:55.677356image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:57.497357image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:59.270901image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:01.083221image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:03.093735image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:04.970808image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:39.064754image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:40.851068image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:42.821709image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:44.707569image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:46.487722image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:48.424993image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:50.212234image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:51.958868image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:53.686392image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:55.798355image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:57.623391image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:59.384899image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:01.201220image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:03.209644image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:05.094911image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:39.195986image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:40.975728image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:42.954172image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:44.834041image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:46.611805image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:48.548245image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:50.337197image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:52.080831image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:53.824119image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:55.929356image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:57.747904image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:59.508947image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:01.325217image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:03.332613image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:05.209658image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:39.316700image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:41.090299image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:43.085381image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:44.949818image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:46.731515image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:48.664126image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:50.454635image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:52.192777image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:53.945117image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:56.072386image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:57.871873image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:59.625947image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:01.444922image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:03.447516image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:05.322294image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:39.435582image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:41.202390image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:43.211042image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:45.063290image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:46.846333image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:48.778194image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:50.567633image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:52.299811image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:54.056153image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:56.192355image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:57.986019image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:59.765948image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:01.564921image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:03.557519image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:05.435154image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:39.549302image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:41.311819image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:43.362432image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:45.184335image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:46.957035image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:48.887199image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:50.679667image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:52.407781image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:54.167118image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:56.309356image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:58.094054image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:59.877955image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:01.669920image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:03.664955image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:05.552038image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:39.675359image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:41.428947image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:43.488995image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:45.302245image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:47.070517image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:49.005164image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:50.796221image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:52.517810image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:54.285434image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:56.431945image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:58.208893image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:08:59.992949image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:01.781343image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-24T20:09:03.779125image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2022-11-24T20:09:10.309036image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-24T20:09:10.516150image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-24T20:09:11.064064image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-24T20:09:11.266060image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-24T20:09:05.744480image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-24T20:09:06.042829image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

song_idsong_namesong_popularitysong_duration_msacousticnessdanceabilityenergyinstrumentalnesskeylivenessloudnessaudio_modespeechinesstempotime_signatureaudio_valence
02231Maneater - Radio Edit582709460.01530.7280.5180.000042110.103-11.11400.03888.70840.833
118680Better Off Alone621928350.01550.9030.4980.00000880.105-6.37910.108121.97440.439
216908Song That I Heard592376660.7730.2670.3560.0017680.167-12.10310.031792.27240.314
310155Zumba602639730.002050.7290.8940.02150.128-3.49410.0397124.99240.832
418150Bumper To Bumper312259330.1480.7710.7350.050.288-9.16200.116119.98340.385
516694Eighties382311330.0002170.4040.9460.0016820.411-11.56200.088151.71640.437
6331I'm A Man141761600.0380.550.9290.090.135-5.76510.0815129.01140.643
718702Body861632160.04760.7520.7640.00009410.0543-4.39910.038121.95840.582
816364God's Gonna Cut You Down661585730.8770.6110.4790.00000650.106-8.07400.11782.36640.79
917995Peaches N Cream482842130.02470.830.7220.00000100.0741-6.09910.0595109.98740.29

Last rows

song_idsong_namesong_popularitysong_duration_msacousticnessdanceabilityenergyinstrumentalnesskeylivenessloudnessaudio_modespeechinesstempotime_signatureaudio_valence
131749977Confidently Lost692098850.2720.6660.390.00000950.111-8.57510.0838113.86640.325
1317514440El Gato Volador541138670.05290.9580.8070.00065460.924-8.79310.241109.23940.87
131768554Black or White - Single Version712028530.08240.7410.8940.052740.089-3.82610.0495114.86940.96
131777280WEAKEND471678000.2620.6280.5050.0035500.132-8.09110.164137.0440.0679
131787462Leave Me Alone691956370.1070.7920.7430.070.183-2.80610.0851150.02440.742
131798730WORKIN ME861696200.1820.790.6290.00000780.338-4.05510.142170.02340.267
1318017365Nobody Wants to Be Lonely - Ricky Martin with Christina Aguilera532527060.005790.6350.8540.0083100.0623-5.0200.0612100.85140.59
131813236Sinking Ship532474930.950.370.1740.002940.11-19.31600.037792.7540.181
131823161The Light761796530.01780.830.5770.00008140.0436-5.15600.25692.97340.63
131838240Do Wah Diddy Diddy641434000.2620.6610.5570.040.0557-8.24410.049125.44540.957